Share:


An integrated multi-objectives optimization approach on modelling pavement maintenance strategies for pavement sustainability

    Ankang Ji Affiliation
    ; Xiaolong Xue Affiliation
    ; Yuna Wang Affiliation
    ; Xiaowei Luo Affiliation
    ; Minggong Zhang Affiliation

Abstract

Addressing the multi-dimensional challenges to promote pavement sustainability requires the development of an optimization approach by simultaneously taking into account future pavement conditions for pavement maintenance with the capability to search and determine optimal pavement maintenance strategies. Thus, this research presents an integrated approach based on the Markov chain and Particle swarm optimization algorithm which aims to consider the predicted pavement condition and optimize the pavement maintenance strategies during operation when applied in the maintenance management of a road pavement section. A case study is conducted for testing the capability of the proposed integrated approach based on two maintenance perspectives. For case 1, maintenance activities mainly occur in TM20, TM31, and TM41, with the maximum maintenance mileage reaching 88.49 miles, 50.89 miles, and 20.91 miles, respectively. For case 2, the largest annual maintenance cost in the first year is $15.16 million with four types of maintenance activities. Thereafter, the maintenance activities are performed at TM10, TM31, and TM41, respectively. The results obtained, compared with the linear program, show the integrated approach is effective and reliable for determining the maintenance strategy that can be employed to promote pavement sustainability.

Keyword : pavement maintenance management, maintenance strategy, pavement sustainability, Markov chain, Particle swarm optimization

How to Cite
Ji, A., Xue, X., Wang, Y., Luo, X., & Zhang, M. (2020). An integrated multi-objectives optimization approach on modelling pavement maintenance strategies for pavement sustainability. Journal of Civil Engineering and Management, 26(8), 717-732. https://doi.org/10.3846/jcem.2020.13751
Published in Issue
Nov 5, 2020
Abstract Views
125
PDF Downloads
106
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Ahmed, K., Al-Khateeb, B., & Mahmood, M. (2019a). Application of chaos discrete particle swarm optimization algorithm on pavement maintenance scheduling problem. Cluster Computing, 22(2), 4647–4657. https://doi.org/10.1007/s10586-018-2239-3

Ahmed, K., Al-Khateeb, B., & Mahmood, M. (2019b). A multiobjective particle swarm optimization for pavement maintenance with chaos and discrete. Journal of Southwest Jiaotong University, 54(3). https://doi.org/10.35741/issn.0258-2724.54.3.5

Akyildiz, S. (2008). Development of new network-level optimization model for Salem district pavement maintenance programming. Virginia Tech. http://hdl.handle.net/10919/34827

Alothaimeen, I., & Arditi, D. (2019). Overview of multi-objective optimization approaches in construction project management. In Multi-criteria optimization – Pareto-optimal and related principles. IntechOpen. https://doi.org/10.5772/intechopen.88185

Barone, G., & Frangopol, D. M. (2014). Life-cycle maintenance of deteriorating structures by multi-objective optimization involving reliability, risk, availability, hazard and cost. Structural Safety, 48, 40–50. https://doi.org/10.1016/j.strusafe.2014.02.002

Biondini, F., & Frangopol, D. M. (2016). Life-cycle performance of deteriorating structural systems under uncertainty. Journal of Structural Engineering, 142(9), F4016001. https://doi.org/10.1061/(ASCE)ST.1943-541X.0001544

Bosurgi, G., & Trifirò, F. (2005). A model based on artificial neural networks and genetic algorithms for pavement maintenance management. International Journal of Pavement Engineering, 6(3), 201–209. https://doi.org/10.1080/10298430500195432

Chang, J. R. (2013). Particle swarm optimization method for optimal prioritization of pavement sections for maintenance and rehabilitation activities. Applied Mechanics and Materials, 343, 43–49. https://doi.org/10.4028/www.scientific.net/AMM.343.43

Chootinan, P., Chen, A., Horrocks, M. R., & Bolling, D. (2006). A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach. Transportation Research Part A – Policy and Practice, 40(9), 725–743. https://doi.org/10.1016/j.tra.2005.12.003

Chou, J.-S., & Le, T.-S. (2011). Reliability-based performance simulation for optimized pavement maintenance. Reliability Engineering & System Safety, 96(10), 1402–1410. https://doi.org/10.1016/j.ress.2011.05.005

Chou, J.-S., & Le, T.-S. (2014). Probabilistic multiobjective optimization of sustainable engineering design. KSCE Journal of Civil Engineering, 18(4), 853–864. https://doi.org/10.1007/s12205-014-0373-x

Eberhart, R., & Kennedy, J. (1995). A new optimizer using particle swarm theory. In Proceedings of the Sixth International Symposium on Micro Machine and Human Science (IEEE), Nagoya, Japan. https://doi.org/10.1109/MHS.1995.494215

Elhadidy, A. A., Elbeltagi, E. E., & Ammar, M. A. (2015). Optimum analysis of pavement maintenance using multi-objective genetic algorithms. HBRC Journal, 11(1), 107–113. https://doi.org/10.1016/j.hbrcj.2014.02.008

Feng, K., Lu, W., Chen, S., & Wang, Y. (2018). An integrated environment–cost–time optimisation method for construction contractors considering global warming. Sustainability, 10(11), 4207. https://doi.org/10.3390/su10114207

Figueredo, G. P., Owa, K., & John, R. (2020). Multi-objective optimization for time-based preventive maintenance within the transport network: a review. Academic and Library Computing.

Gao, H., & Zhang, X. (2013). A Markov‐based road maintenance optimization model considering user costs. Computer‐Aided Civil and Infrastructure Engineering, 28(6), 451–464. https://doi.org/10.1111/mice.12009

Gopalakrishnan, K. (2010). Neural network–swarm intelligence hybrid nonlinear optimization algorithm for pavement moduli back-calculation. Journal of Transportation Engineering, 136(6), 528–536. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000128

Gopalakrishnan, K. (2013). Particle swarm optimization in civil infrastructure systems: state-of-the-art review. Chapter 3 in A. H. Gandomi, X.-S. Yang, S. Talatahari, & A. H. Alavi (Eds.), Metaheuristic applications in structures and infrastructures (pp. 49–76). Elsevier Inc. https://doi.org/10.1016/B978-0-12-398364-0.00003-6

Jesus, M., Akyildiz, S., Bish, D. R., & Krueger, D. A. (2011). Network-level optimization of pavement maintenance renewal strategies. Advanced Engineering Informatics, 25(4), 699–712. https://doi.org/10.1016/j.aei.2011.08.002

Khavandi Khiavi, A., & Mohammadi, H. (2018). Multiobjective optimization in pavement management system using NSGA-II method. Journal of Transportation Engineering, Part B: Pavements, 144(2), 04018016. https://doi.org/10.1061/JPEODX.0000041

Kim, S., Ge, B., & Frangopol, D. M. (2019). Effective optimum maintenance planning with updating based on inspection information for fatigue-sensitive structures. Probabilistic Engineering Mechanics, 58, 103003. https://doi.org/10.1016/j.probengmech.2019.103003

Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety, 91(9), 992–1007. https://doi.org/10.1016/j.ress.2005.11.018

Lamptey, G., Labi, S., & Li, Z. (2008). Decision support for optimal scheduling of highway pavement preventive maintenance within resurfacing cycle. Decision Support Systems, 46(1), 376–387. https://doi.org/10.1016/j.dss.2008.07.004

Lea, J. D., & Harvey, J. T. (2015). Using spatial statistics to characterise pavement properties. International Journal of Pavement Engineering, 16(3), 239–255. https://doi.org/10.1080/10298436.2014.942856

Lethanh, N., & Adey, B. T. (2013). Use of exponential hidden Markov models for modelling pavement deterioration. International Journal of Pavement Engineering, 14(7), 645–654. https://doi.org/10.1080/10298436.2012.715647

Lethanh, N., Kaito, K., & Kobayashi, K. (2015). Infrastructure deterioration prediction with a poisson hidden Markov model on time series data. Journal of Infrastructure Systems, 21(3), 04014051. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000242

Mandiartha, P., Duffield, C. F., Thompson, R. G., & Wigan, M. R. (2017). Measuring pavement maintenance effectiveness using Markov chains analysis. Structure and Infrastructure Engineering, 13(7), 844–854. https://doi.org/10.1080/15732479.2016.1212901

Moazami, D., Behbahani, H., & Muniandy, R. (2011). Pavement rehabilitation and maintenance prioritization of urban roads using fuzzy logic. Expert Systems with Applications, 38(10), 12869–12879. https://doi.org/10.1016/j.eswa.2011.04.079

Morcous, G., & Lounis, Z. (2005). Maintenance optimization of infrastructure networks using genetic algorithms. Automation in Construction, 14(1), 129–142. https://doi.org/10.1016/j.autcon.2004.08.014
Moreira, A. V., Tinoco, J., Oliveira, J. R., & Santos, A. (2018). An application of Markov chains to predict the evolution of performance indicators based on pavement historical data. International Journal of Pavement Engineering, 19(10), 937–948. https://doi.org/10.1080/10298436.2016.1224412

Noortwijk, J. M., & Frangopol, D. M. (2004). Two probabilistic life-cycle maintenance models for deteriorating civil infrastructures. Probabilistic Engineering Mechanics, 19(4), 345–359. https://doi.org/10.1016/j.probengmech.2004.03.002

Neal, B., & Pro, P. (2020). The dererioration of asphalt pavement and its causes. PavementPro.com. https://www.pavemanpro.com/article/deterioration_asphalt_causes/

Osorio-Lird, A., Chamorro, A., Videla, C., Tighe, S., & TorresMachi, C. (2018). Application of Markov chains and Monte Carlo simulations for developing pavement performance models for urban network management. Structure and Infrastructure Engineering, 14(9), 1169–1181. https://doi.org/10.1080/15732479.2017.1402064

Pérez-Acebo, H., Bejan, S., & Gonzalo-Orden, H. (2018). Transition probability matrices for flexible pavement deterioration models with half-year cycle time. International Journal of Civil Engineering, 16(9), 1045–1056. https://doi.org/10.1007/s40999-017-0254-z

Panda, T. R., & Swamy, A. K. (2018). An improved artificial bee colony algorithm for pavement resurfacing problem. International Journal of Pavement Research and Technology, 11(5), 509–516. https://doi.org/10.1016/j.ijprt.2018.04.001

Pandey, M., Yuan, X.-X., & Van Noortwijk, J. (2009). The influence of temporal uncertainty of deterioration on life-cycle management of structures. Structure and Infrastructure Engineering, 5(2), 145–156. https://doi.org/10.1080/15732470601012154

Pantuso, A., Flintsch, G. W., Katicha, S. W., & Loprencipe, G. (2019). Development of network-level pavement deterioration curves using the linear empirical Bayes approach. International Journal of Pavement Engineering, 20, 1646912. https://doi.org/10.1080/10298436.2019.1646912

Ramachandran, S., Rajendran, C., & Amirthalingam, V. (2019). Decision support system for the maintenance management of road network considering multi-criteria. International Journal of Pavement Research and Technology, 12(3), 325–335. https://doi.org/10.1007/s42947-019-0039-7

Roads & Bridges. (2020). The industry resource for the road and bridge construction market. https://www.roadsbridges.com/

Saha, P., Ksaibati, K., & Atadero, R. (2017). Developing pavement distress deterioration models for pavement management system using Markovian probabilistic process. Advances in Civil Engineering, Article ID 8292056. https://doi.org/10.1155/2017/8292056

Santos, J., Ferreira, A., & Flintsch, G. (2017). A multi-objective optimization-based pavement management decision-support system for enhancing pavement sustainability. Journal of Cleaner Production, 164, 1380–1393. https://doi.org/10.1016/j.jclepro.2017.07.027

Santos, J., Ferreira, A., Flintsch, G., & Cerezo, V. (2018). A multi-objective optimisation approach for sustainable pavement management. Structure and Infrastructure Engineering, 14(7), 854–868. https://doi.org/10.1080/15732479.2018.1436571

Santos, J., Ferreira, A., & Flintsch, G. (2019). An adaptive hybrid genetic algorithm for pavement management. International Journal of Pavement Engineering, 20(3), 266–286. https://doi.org/10.1080/10298436.2017.1293260

Singh, A. P., Sharma, A., Mishra, R., Wagle, M., & Sarkar, A. K. (2018). Pavement condition assessment using soft computing techniques. International Journal of Pavement Research and Technology, 11(6), 564–581. https://doi.org/10.1016/j.ijprt.2017.12.006

Surendrakumar, K., Prashant, N., & Mayuresh, P. (2013). Application of Markovian probabilistic process to develop a decision support system for pavement maintenance management. International Journal of Scientific & Technology Research, 2(8), 295–303.

Suresh, K., & Kumarappan, N. (2013). Hybrid improved binary particle swarm optimization approach for generation maintenance scheduling problem. Swarm and Evolutionary Computation, 9, 69–89. https://doi.org/10.1016/j.swevo.2012.11.003

Tabatabaee, N., & Ziyadi, M. (2013). Bayesian approach to updating Markov-based models for predicting pavement performance. Transportation Research Record: Journal of Transportation Research Board, 2366, 34–42. https://doi.org/10.3141/2366-04

Tayebi, N. R., Nejad, F. M., & Mola, M. (2014). Comparison between GA and PSO in analyzing pavement management activities. Journal of Transportation Engineering, 140(1), 99–104. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000590

Tee, K. F., Ekpiwhre, E., & Yi, Z. (2018). Degradation modelling and life expectancy using Markov chain model for carriageway. International Journal of Quality & Reliability Management, 25(6), 1268–1288. https://doi.org/10.1108/IJQRM-06-2017-0116

Torres-Machi, C., Pellicer, E., Yepes, V., & Chamorro, A. (2017). Towards a sustainable optimization of pavement maintenance programs under budgetary restrictions. Journal of Cleaner Production, 148, 90–102. https://doi.org/10.1016/j.jclepro.2017.01.100

U.S. Department of Transportation, Federal Highway Administration. (2020). Impact of environmental factors on pavement performance in the absence of heavy loads. https://www.fhwa.dot.gov/publications/research/infrastructure/pavements/16084/16084.pdf

Vyas, V., Singh, A. P., & Srivastava, A. (2019). Entropy-based fuzzy SWOT decision-making for condition assessment of airfield pavements. International Journal of Pavement Engineering, 20, 1–12. https://doi.org/10.1080/10298436.2019.1671590

Wang, Y., Feng, K., & Lu, W. (2017). An environmental assessment and optimization method for contractors. Journal of Cleaner Production, 142, 1877–1891. https://doi.org/10.1016/j.jclepro.2016.11.097

Yang, C., Remenyte-Prescott, R., & Andrews, J. D. (2015). Pavement maintenance scheduling using genetic algorithms. International Journal of Performability Engineering, 11(2), 973–1318.

Yepes, V., Torres-Machi, C., Chamorro, A., & Pellicer, E. (2016). Optimal pavement maintenance programs based on a hybrid greedy randomized adaptive search procedure algorithm. Journal of Civil Engineering and Management, 22(4), 540–550. https://doi.org/10.3846/13923730.2015.1120770

Yu, B., Gu, X., Ni, F., & Guo, R. (2015). Multi-objective optimization for asphalt pavement maintenance plans at project level: Integrating performance, cost and environment. Transportation Research Part D: Transport and Environment, 41, 64–74. https://doi.org/10.1016/j.trd.2015.09.016

Yu, B., Lu, Q., & Xu, J. (2013). An improved pavement maintenance optimization methodology: Integrating LCA and LCCA. Transportation Research Part A: Policy and Practice, 55, 1–11. https://doi.org/10.1016/j.tra.2013.07.004

Yuan, X., Zhang, B., Wang, P., Liang, J., Yuan, Y., Huang, Y., & Lei, X. (2017). Multi-objective optimal power flow based on improved strength Pareto evolutionary algorithm. Energy, 122, 70–82. https://doi.org/10.1016/j.energy.2017.01.071